A hierarchical coevolutionary method to support brain-lesion modelling

The current work addresses the development of cognitive utilities in artificial organisms, a topic that has attracted many research efforts recently. In our approach, neural network-based agent structures are employed to represent distinct brain areas. We introduce a hierarchical collaborative coevolutionary (HCCE) approach to design autonomous, yet cooperating agents. Thus, partial brain models consisting of many substructures can be designed. Replication of lesion studies is used as a means to increase reliability of brain model, highlighting the distinct roles of agents. The HCCE is appropriately designed to support systematic modelling of brain structures, able to reproduce biological lesion data. The proposed approach designs cooperating agents properly, by considering the desired pre- and post- lesion performance of the model. The effectiveness of the proposed approach is illustrated on the design of a computational model of primary motor cortex and premotor cortex interactions in the mammalian brain. The model is successfully tested in driving a simulated robot, with different pre- and post- lesion performance.